Created on Monday, 23 September 2013 12:08 | Published on Monday, 23 September 2013 12:08 | | | Hits: 2149

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The last developments of rCUDA, including a thorough performance analysis, were presented on September 12th at the HPC Advisory Council Spain Conference 2013, held in Barcelona (Spain) and co-hosted by the Barcelona Supercomputer Center. The slides of the presentation are available here. You can also access a video with the presentation in this link.

Created on Friday, 06 September 2013 13:43 | Published on Friday, 06 September 2013 13:43 | | | Hits: 2090

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The rCUDA remote GPU virtualization technology, whose last release also supports the ARM processor architecture, has been now leveraged to execute the LAMMPS and CUDASW++ applications in ARM-based systems. More specifically, the Tegra 3 ARM Cortex A9 quad-core CPUs (1.4 GHz) present in the NVIDIA Development Kits CARMA and KAYLA have been used to execute the CPU code of these applications, whereas the GPU code has been offloaded to an NVIDIA GeForce GTX480 “Fermi” GPU installed in a remote regular Xeon-based system. Results clearly show that by using rCUDA and remote accelerators, noticeable performance improvements are achieved over using the local NVIDIA Quadro 1000M GPU already present in the CARMA system, despite of using a traditional 1Gbps Ethernet network.

Created on Friday, 05 July 2013 11:39 | Published on Friday, 05 July 2013 11:39 | | | Hits: 2144

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The rCUDA team has been working for the last 6 months on porting the rCUDA middleware to the ARM processor architecture. The port has been made on the CARMA and KAYLA nVIDIA development platforms. We are now completing the first release of rCUDA for this low-power processor, which will be available very soon. Our tests include running an application on the ARM processor that demands GPGPU services on an x86 remote rCUDA server. Conversely, it is also possible to locate the GPU on an ARM-based server and demand GPGPU services from an x86 computer.

Created on Tuesday, 30 July 2013 13:56 | Published on Tuesday, 30 July 2013 13:56 | | | Hits: 2355

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The rCUDA team has been working during the last months in order to create an rCUDA module for the SLURM scheduler. This scheduler, which is used in many clusters around the world, efficiently dispatches computing jobs to the different nodes of the cluster. However, when a job requires the use of one or several GPUs, the SLURM scheduler assumes that those GPUs will be local to the node where the job is targeted, thus hindering the use of remote GPU virtualization frameworks such as rCUDA. With the new module created by the rCUDA team, the SLURM scheduler is aware of the use of remote GPU virtualizaton, therefore making possible sharing the GPUs available in the cluster among the several applications demanding them, independently of the exact node where the application is being executed and also independently of the exact node where the GPU is located. The new module allows to schedule GPUs in two ways: in an exclusive way, or concurrently sharing the GPUs among several applications. Notice that in both cases the use of remote GPUs is feasible.

Created on Monday, 01 July 2013 12:32 | Published on Monday, 01 July 2013 12:32 | | | Hits: 2577

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The rCUDA remote GPU virtualization framework was recently updated in order to provide support for the last CUDA 5.0 release. Now, with the launch of the new CUDA 5.5 release candidate, the rCUDA team has tested the rCUDA middleware with this new CUDA release and is working in updating rCUDA to the new CUDA version.

Created on Tuesday, 23 July 2013 10:50 | Published on Tuesday, 23 July 2013 10:50 | | | Hits: 2508

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The rCUDA team is glad to announce that its remote GPU virtualization technology now supports the ARM processor architecture. The new release of rCUDA for this low-power processor has been developed for the Ubuntu 11.04 and Ubuntu 12.04 ARM linux distributions. With this new rCUDA release, it is also possible to leverage hybrid platforms where the application uses ARM CPUs while requesting acceleration services provided by remote GPUs installed in x86 nodes. The opposite is also possible: an application running in an x86 computer can access remote GPUs attached to ARM systems